Immune dysfunction signatures predict outcomes and define checkpoint blockade–unresponsive microenvironments in acute myeloid leukemia
Sergio Rutella, … , Ivana Gojo, Leo Luznik
Sergio Rutella, … , Ivana Gojo, Leo Luznik
Published September 13, 2022
Citation Information: J Clin Invest. 2022;132(21):e159579. https://doi.org/10.1172/JCI159579.
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Clinical Medicine Hematology

Immune dysfunction signatures predict outcomes and define checkpoint blockade–unresponsive microenvironments in acute myeloid leukemia

Abstract

Background Immune exhaustion and senescence are dominant dysfunctional states of effector T cells and major hurdles for the success of cancer immunotherapy. In the current study, we characterized how acute myeloid leukemia (AML) promotes the generation of senescent-like CD8+ T cells and whether they have prognostic relevance.METHODS We analyzed NanoString, bulk RNA-Seq and single-cell RNA-Seq data from independent clinical cohorts comprising 1,896 patients treated with chemotherapy and/or immune checkpoint blockade (ICB).Results We show that senescent-like bone marrow CD8+ T cells were impaired in killing autologous AML blasts and that their proportion negatively correlated with overall survival (OS). We defined what we believe to be new immune effector dysfunction (IED) signatures using 2 gene expression profiling platforms and reported that IED scores correlated with adverse-risk molecular lesions, stemness, and poor outcomes; these scores were a more powerful predictor of OS than 2017-ELN risk or leukemia stem cell (LSC17) scores. IED expression signatures also identified an ICB-unresponsive tumor microenvironment and predicted significantly shorter OS.Conclusion The IED scores provided improved AML-risk stratification and could facilitate the delivery of personalized immunotherapies to patients who are most likely to benefit.TRIAL REGISTRATION ClinicalTrials.gov; NCT02845297.FUNDING John and Lucille van Geest Foundation, Nottingham Trent University’s Health & Wellbeing Strategic Research Theme, NIH/NCI P01CA225618, Genentech-imCORE ML40354, Qatar National Research Fund (NPRP8-2297-3-494).

Authors

Sergio Rutella, Jayakumar Vadakekolathu, Francesco Mazziotta, Stephen Reeder, Tung-On Yau, Rupkatha Mukhopadhyay, Benjamin Dickins, Heidi Altmann, Michael Kramer, Hanna A. Knaus, Bruce R. Blazar, Vedran Radojcic, Joshua F. Zeidner, Andrea Arruda, Bofei Wang, Hussein A. Abbas, Mark D. Minden, Sarah K. Tasian, Martin Bornhäuser, Ivana Gojo, Leo Luznik

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Figure 5

Predictive ability of immune effector dysfunction gene programs in the TCGA-AML cohort.

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Predictive ability of immune effector dysfunction gene programs in the T...
(A) Area under the receiver operating characteristic (AUROC) curve measuring the predictive ability of the prognostic index (PI24) genes for overall survival (n = 157 TCGA cases). (B) Forest plot (ggforest function in survminer package in R) of pretreatment features (WBC count at diagnosis, percentage of bone marrow blasts, FLT3-ITD and NPM1 mutational status, patient age at diagnosis), and RNA-based scores associated with survival in multivariate Cox proportional hazard analyses (PI24, LSC17, and IFN scores; refs. 2, 47, 52). HR = hazard ratio for death. (C) Kaplan-Meier estimates of relapse-free survival (RFS) in patients with TCGA-AML with above-median and below-median PI24 scores, which were calculated using β values from Cox regression analyses of gene expression and patient survival (56). Survival curves were compared using the log-rank test. (D) Kaplan-Meier estimates of OS in TCGA-AML patients with above-median and below-median PI24 scores. Survival curves were compared using the log-rank test.